Bibliographic record
Abstract
An ontology in philosophy is a description of what exists; an ontology in information science is the formal specification of a domain for information organization. A top-level ontology is an ontology of high-level abstractions that is meant to provide a standard for other ontologies to make them interoperable. Top-level ontologies commit to certain philosophical assumptions in the worlds they model, such as naive scientific realism, or the choice to model only those worlds which “actually” exist. This paper uses a comparison of Basic Formal Ontology (BFO) and Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) to investigate whether naive scientific realism is useful as a philosophical stance in ontological modelling. It investigates the specific philosophical and technical features of DOLCE which make it unique. BFO and DOLCE are compared, as well as a miniature literature review of comparisons between other top-level ontologies. Then it describes the history of DOLCE and BFO’s development in the context of the collaboration between Barry Smith and Nicola Guarino. Finally, it examines the application of DOLCE in various domains, such as “sweetening” WordNet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.014 | 0.039 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".